pacman::p_load(readxl, gifski, gapminder,
plotly, gganimate, tidyverse)Hands-on_Ex03 Part 2 : Programming Animated Statistical Graphics with R
4.1 Overview
This section will use gganimate and plotly r packages to create animated data visualisation At the same time, I will also learn how to (i) reshape data by using tidyr package, and (ii) process, wrangle and transform data by using dplyr package.
4.2 Installing R pacakges
4.2.1 Importing the data
col <- c("Country", "Continent")
globalPop <- read_xls("GlobalPopulation.xls",
sheet="Data") %>%
mutate_at(col, as.factor) %>%
mutate(Year = as.integer(Year))4.3 By using gganimate methods
transition_() defines how the data should be spread out and how it relates to itself across time.
view_() defines how the positional scales should change along the animation.
shadow_() defines how data from other points in time should be presented in the given point in time.
enter_()/exit_*() defines how new data should appear and how old data should disappear during the course of the animation.
ease_aes() defines how different aesthetics should be eased during transitions
4.3.1 Building a static population bubble plot
ggplot(globalPop, aes(x = Old, y = Young,
size = Population,
colour = Country)) +
geom_point(alpha = 0.7,
show.legend = FALSE) +
scale_colour_manual(values = country_colors) +
scale_size(range = c(2, 12)) +
labs(title = 'Year: {frame_time}',
x = '% Aged',
y = '% Young') 
4.3.2 Building the animated bubble plot
transition_time() of gganimate is used to create transition through distinct states in time (i.e. Year).
ease_aes() is used to control easing of aesthetics. The default is linear. Other methods are: quadratic, cubic, quartic, quintic, sine, circular, exponential, elastic, back, and bounce.
ggplot(globalPop, aes(x = Old, y = Young,
size = Population,
colour = Country)) +
geom_point(alpha = 0.7,
show.legend = FALSE) +
scale_colour_manual(values = country_colors) +
scale_size(range = c(2, 12)) +
labs(title = 'Year: {frame_time}',
x = '% Aged',
y = '% Young') +
transition_time(Year) +
ease_aes('linear') 
4.4.1 Animated Data Visualisation: plotly
gg <- ggplot(globalPop,
aes(x = Old,
y = Young,
size = Population,
colour = Country)) +
geom_point(aes(size = Population,
frame = Year),
alpha = 0.7,
show.legend = FALSE) +
scale_colour_manual(values = country_colors) +
scale_size(range = c(2, 12)) +
labs(x = '% Aged',
y = '% Young')Warning in geom_point(aes(size = Population, frame = Year), alpha = 0.7, :
Ignoring unknown aesthetics: frame
ggplotly(gg)Warning in p$x$data[firstFrame] <- p$x$frames[[1]]$data: number of items to
replace is not a multiple of replacement length
Appropriate ggplot2 functions are used to create a static bubble plot. The output is then saved as an R object called gg.
ggplotly() is then used to convert the R graphic object into an animated svg object.
4.4.2 Building an animated bubble plot: plot_ly() method
bp <- globalPop %>%
plot_ly(x = ~Old,
y = ~Young,
size = ~Population,
color = ~Continent,
sizes = c(2, 100),
frame = ~Year,
text = ~Country,
hoverinfo = "text",
type = 'scatter',
mode = 'markers'
) %>%
layout(showlegend = FALSE)
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